Morning Brain: Real-world Neural Evidence That High School Class Times Matter

Suzanne Dikker; Saskia Haegens; Dana Bevilacqua; Ido Davidesco; Lu Wan; Lisa Kaggen; James McClintock; Kim Chaloner; Mingzhou Ding; Tessa West; David Poeppel

Disclosures

Soc Cogn Affect Neurosci. 2020;15(11):1193-1202. 

In This Article

Abstract and Introduction

Abstract

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep–wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students' resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents' brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn.

Introduction

Among all the ups and downs associated with the high school experience, most readers will distinctly recall trying (and failing) to make it to school on time, especially during the winter season. Most younger kids are early risers, but something drastic happens to sleep/wake patterns around the onset of puberty (Wolfson and Carskadon, 1998; Roenneberg et al., 2004; Crowley et al., 2007): as children enter adolescence, they tend to shift toward later bedtimes. While variations in chronotypes exist across all ages, this sharp change in circadian rhythms toward 'eveningness' is so consistent and distinct that it is often regarded as a reliable biomarker for the onset of adolescence (Carskadon et al., 1998, 2006; Dewald et al., 2010; Preckel et al., 2011; Crowley et al., 2014), resulting in a stark mismatch between biologically preferred and socially imposed sleep–wake hours (Crowley et al., 2007; Dewald et al., 2010; Wahlstrom et al., 2014; Kelley et al., 2017) for most teenagers. In fact, chronically inadequate sleep in adolescents is now considered a public health epidemic (Wheaton et al., 2016).

It is well known that low levels of alertness resulting from sleep-related factors are associated with a reduction in the ability to focus, which, in turn, affects learning and task performance (Chee and Choo, 2004; Lim and Dinges, 2010; Sievertsen et al., 2016). To highlight just one example, performance accuracy in adults on certain basic cognitive and neurobehavioral tasks is more impaired after mild sleep deprivation than after alcohol intake that exceeds the levels for legal intoxication (Lamond and Dawson, 1999). For teenagers, various factors connected to sleep (e.g. sleep time, circadian rhythms and sleep loss) are shown to influence levels of focus, alertness and mood, both within and outside of laboratory contexts (Wolfson and Carskadon, 1998; Preckel et al., 2011; Short et al., 2013; Goldin et al., 2020).

Although it can be difficult to disentangle whether low levels of alertness in the morning in high schoolers are due to sleep loss or chronotype (i.e. either adolescents go to bed late and have slept only a few hours by the time they have to wake up, or they are woken up at a bad moment in their natural sleep cycle), the overall observation is that adolescents are less alert in the morning than other age groups and that this lack of alertness leads to a reduction in (cognitive) performance in the morning. Perhaps most strikingly, it has been reported that car accidents in 16–19-year-olds decreased by 65–70% when school start time was delayed from 7:55 to 8:25 am (Wahlstrom et al., 2014).

In light of these findings, it is unsurprising that studies consistently find that age-dependent circadian rhythms (as well as chronotype) predict school experience and academic performance (Dewald et al., 2010; Preckel et al., 2011; Short et al., 2013; Vollmer et al., 2013; Wahlstrom et al., 2014). For example, research has shown that sleep-restricted adolescents exhibit poorer cognitive performance and higher levels of sleepiness in the early morning (Short et al., 2013; Lo et al., 2016). As a rather unfortunate coincidence, adolescents spend a sizeable chunk of their mornings attempting to retain information. This has sparked an ongoing public debate about high school start times. In fact, delaying school start times by even 50 min appears to have a significant positive effect on student achievement (Carrell et al., 2011; also see Minges and Redeker, 2016; Bowers and Moyer, 2017). However, effect sizes in these data are often small, and results are largely dependent on explicit self-report measures (including parental reports; Dewald et al., 2010), which are not always reliable. One possible additional source of information may come from (neuro)physiological data, which has been suggested to constitute a better predictor of behavior than self-report in some cases (Berkman and Falk, 2012; Thorson et al., 2018). With the advance of wearable technology, researchers are now able to collect implicit biophysiological data during everyday activities (Debener et al., 2012), including sleep (de Zambotti et al., 2016). For example, a recent study used wearable activity trackers to assess the relationship between sleep patterns and academic performance in college students (Okano et al., 2019).

Here, we extend on this body of work by focusing on neurophysiological data collected from teenagers as an implicit, real-time measure of their alertness during naturalistic daytime activities. Specifically, we recorded electroencephalogram (EEG) from high school students during their regular classes (consisting of a combination of teacher-led lectures and educational videos) throughout the school day (early morning, mid-morning and afternoon) in two different New York City high schools (Figure 1; Bevilacqua et al., 2019; Dikker et al., 2017).

Figure 1.

Experimental setup. (A) Study timeline. EEG activity was recorded from 2 groups of 12 students at 2 separate high schools. Seventeen recording days were scheduled throughout the semester (school 1, 11 recording days; school 2, 6 recording days). Recording sessions were equally distributed across three different class times: early morning, mid-morning and mid-afternoon (AM1, AM2, PM). (B) Experimental procedure of a typical recording day. EEG activity was recorded during four teaching blocks in addition to a resting state segment where students were facing the wall. Lecture and video teaching activities were consistently administered during all 17 recording days across both schools; other tasks varied between days (see, for details, Dikker et al., 2017; Bevilacqua et al., 2019). Student alpha power activity was averaged for each student during each class activity separately (marked in red). (C) Illustration of experimental setup in the classroom with 12 students wearing the EMOTIV EPOC headset. These portable devices offer a rich opportunity to involve students both as participants and as experimenters (Dikker et al., 2017; Bevilacqua et al., 2019).

Although EEG is often employed as a measure of focus/alertness (Corsi-Cabrera et al., 1992; Cajochen et al., 1995; Horne and Baulk, 2004; Lockley et al., 2006), to our knowledge, we are the first to do so in a real-world school environment. This approach uniquely positioned us to investigate how student brain activity varies as a function of class time and how such neural changes might relate to self-reported focus and academic performance. We focused our analysis on students' brain activity in the alpha frequency range (~10 Hz). The alpha rhythm, traditionally associated with cortical idling (Adrian and Matthews, 1934; Pfurtscheller et al., 1996), is typically negatively correlated with selective attention and is thought to reflect a mechanism of active inhibition, such that increased alpha activity functions to suppress (distracting) input whereas decreased alpha facilitates processing (Klimesch et al., 2007; Jensen and Mazaheri, 2010; Haegens et al., 2011a 2011b). In addition to reflecting local task-related focus, a person's global alpha power is strongly related to overall vigilance or attentiveness (Haslum and Gale, 1973; Linkenkaer-Hansen et al., 2004; Lakatos et al., 2016; Crunelli et al., 2018). We studied how alpha power varied over the course of the day and during different class activities at the group level, comparing alpha activity during teacher-led activities (lectures), educational videos and rest (silently looking at the wall for 2 min).

Furthermore, we linked brain activity to both self-reported attentiveness ('how focused are you right now?') and quiz scores. We hypothesized that alpha power would be highest early in the morning and inversely related to students' quiz scores (Preckel et al., 2011). In addition, following previous findings that reported that students are more engaged during videos than lectures (Dikker et al., 2017; Bevilacqua et al., 2019), we predicted that students would exhibit higher alpha power during lectures than videos.

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